| Because the wireless sensor node of wireless sensor network itself carries limited energy,so energy conservation has been the focus of wireless sensor network research.In many energy-saving technologies,this paper chooses the deployment algorithm and the energy-saving scheduling algorithm to study the wireless sensor network in two directions.Due to the randomness of the random deployment algorithm,the number of nodes deployed is much larger than that of static deployment,which leads to the consumption of more energy.Based on the energy consideration,this paper studies the static deployment technology and proposes a static deployment algorithm.Considering the influence of the residual energy of the node and the phase difference of the receiver on the energy consumption of the system,an energy saving scheduling algorithm for cooperative communication is proposed.After the selected node is obtained,the emission coefficient of the node is improved lower.Firstly,the existing deployment algorithm is studied,and the existing deployment algorithms are analyzed with different levels of high complexity.Then,the binary particle swarm algorithm is described in detail,and the binary particle swarm optimization algorithm can effectively solve the problem of binary integer programming.On this basis,a static deployment algorithm based on binary particle swarm algorithm for static deployment of sensor nodes is proposed.The algorithm can effectively reduce the number of sensor nodes in the network while satisfying the user detection requirements.In this paper,the sensor coverage model-truncation probability coverage model used in this paper is given.The coverage of the node is calculated by this model.The fitness function of the algorithm is designed according to the coverage rate and the number of nodes.Improve the binary update algorithm of binary particle swarm algorithm,and add the concept of abandoned useless nodes to solve the fitness function to reduce the number of sensor nodes.The algorithm is a lightweight and efficient deployment algorithm that can get near optimal topology.The simulation results show the influence of the parameters of the binary particle swarm algorithm on the performance,and the results obtained by the algorithm in three cases.Then,the algorithm is compared with the existing deployment algorithm in the calculation of the time and the number of nodes to meet the conditions,etc.,the results show that the algorithm in terms of effectiveness and efficiency have greatly improved.And proposes a cooperative communication energy saving scheduling algorithm based on particle swarm optimization algorithm.Firstly,the influence of the phase difference between the node and the residual energy of the node on the lifetime of the network is analyzed.Since the different phase difference leads to the different signal intensity at the receiving end,it can not be calculated by the ideal model simply,and the residual of each node The energy is different,and the node can not be selected only according to the phase difference.Combining the influence of these two points on the energy consumption of the system,an energy saving scheduling algorithm for cooperative communication is proposed,which effectively solves the problem that some nodes can run out of energy prematurely.Secondly,because of the large energy efficiency of the emission coefficient is a NP problem,and particle swarm optimization algorithm to solve the problem of combinatorial optimization without cumbersome variation and crossover process,the calculation of energy consumption is relatively low,so the use of particle group Algorithm to further optimize it.Particle swarm algorithm by generating multidimensional pa rticles(each particle is a solution),and according to each particle's individual historical optimal solution and population global optimal solution to update the location of each particle and the optimal solution forward speed,The optimal solution is ge nerated by iteration.Then,based on the basic particle swarm algorithm,the influence of the weight on the global optimal solution and the local optimal solution convergence is analyzed and improved,so that the particle swarm optimization algorithm can get the optimal solution faster.In the simulation experiment,the algorithm and the PP algorithm in the network survival time and node residual energy comparison,indicating that the algorithm in the extended survival time has a significant effect,and to achieve a balanced energy consumption between nodes.Particle swarm algorithm and immune genetic algorithm in optimizing the node emission coefficient of the experimental results show that although the immune genetic algorithm in the individual nodes of the emission coefficient than the particle swarm algorithm is lower,but on the whole,the particle swarm algorithm on the node launch The optimization of the coefficients is better than that of the immune genetic algorithm,which reduces the energy consumption. |
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